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    README.md

    Grad-CAM.pytorch

    ​ pytorch 实现Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization

    Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

    1. 依赖
    2. 使用方法
    3. 样例分析
      3.1 单个对象
      3.3 多个对象
    4. 总结
    5. 目标检测-faster-r-cnn
      5.1 detectron2安装
      5.2 测试
      5.3 Grad-CAM结果
      5.4 总结
    6. 目标检测-retinanet
      6.1 detectron2安装
      6.2 测试
      6.3 Grad-CAM结果
      6.4 总结
    7. 目标检测-fcos
      7.1 AdelaiDet安装
      7.2 测试
      7.3 Grad-CAM结果
      7.4 总结

    Grad-CAM整体架构

    Grad-CAM++与Grad-CAM的异同

    依赖

    python 3.6.x
    pytoch 1.0.1+
    torchvision 0.2.2
    opencv-python
    matplotlib
    scikit-image
    numpy

    使用方法

    python main.py --image-path examples/pic1.jpg \
                   --network densenet121 \
                   --weight-path /opt/pretrained_model/densenet121-a639ec97.pth

    参数说明

    • image-path:需要可视化的图像路径(可选,默认./examples/pic1.jpg)

    • network: 网络名称(可选,默认resnet50)

    • weight-path: 网络对应的与训练参数权重路径(可选,默认从pytorch官网下载对应的预训练权重)

    • layer-name: Grad-CAM使用的层名(可选,默认最后一个卷积层)

    • class-id:Grad-CAM和Guided Back Propagation反向传播使用的类别id(可选,默认网络预测的类别)

    • output-dir:可视化结果图像保存目录(可选,默认results目录)

    样例分析

    单个对象

    原始图像

    效果

    network HeatMap Grad-CAM HeatMap++ Grad-CAM++ Guided backpropagation Guided Grad-CAM
    vgg16
    vgg19
    resnet50
    resnet101
    densenet121
    inception_v3
    mobilenet_v2
    shufflenet_v2

    多个对象

    ​ 对应多个图像Grad-CAM++比Grad-CAM覆盖要更全面一些,这也是Grad-CAM++最主要的优势

    原始图像

    效果

    network HeatMap Grad-CAM HeatMap++ Grad-CAM++ Guided backpropagation Guided Grad-CAM
    vgg16
    vgg19
    resnet50
    resnet101
    densenet121
    inception_v3
    mobilenet_v2
    shufflenet_v2

    总结

    • vgg模型的Grad-CAM并没有覆盖整个对象,相对来说resnet和denset覆盖更全,特别是densenet;从侧面说明就模型的泛化和鲁棒性而言densenet>resnet>vgg
    • Grad-CAM++相对于Grad-CAM也是覆盖对象更全面,特别是对于同一个类别有多个实例的情况下,Grad-CAM可能只覆盖部分对象,Grad-CAM++基本覆盖所有对象;但是这仅仅对于vgg而言,想densenet直接使用Grad-CAM也基本能够覆盖所有对象
    • MobileNet V2的Grad-CAM覆盖也很全面
    • Inception V3和MobileNet V2的Guided backpropagation图轮廓很模糊,但是ShuffleNet V2的轮廓则比较清晰

    目标检测-faster-r-cnn

    ​ 有位网友SHAOSIHAN问道怎样在目标检测中使用Grad-CAM;在Grad-CAM和Grad-CAM++论文中都没有提及对目标检测生成CAM图。我想主要有两个原因:

    a) 目标检测不同于分类,分类网络只有一个分类损失,而且所有网络都是一样的(几个类别最后一层就是几个神经元),最后的预测输出都是单一的类别得分分布。目标检测则不同,输出都不是单一的,而且不同的网络如Faster R-CNN, CornerNet,CenterNet,FCOS,它们的建模方式不一样,输出的含义都不相同。所以不会有统一的生成Grad-CAM图的方法。

    b) 分类属于弱监督,通过CAM可以了解网络预测时主要关注的空间位置,也就是"看哪里",对分析问题有实际的价值;而目标检测,本身是强监督,预测边框就直接指示了“看哪里”。

    ​ 这里以detetron2中的faster-rcnn网络为例,生成Grad-CAM图。主要思路是直接获取预测分值最高的边框;将该边框的预测分值反向传播梯度到,该边框对应的proposal 边框的feature map上,生成此feature map的CAM图。

    detectron2安装

    a) 下载

    git clone https://github.com/facebookresearch/detectron2.git

    b) 修改detectron2/modeling/roi_heads/fast_rcnn.py文件中的fast_rcnn_inference_single_image函数,主要是增加索引号,记录分值高的预测边框是由第几个proposal边框生成的;修改后的fast_rcnn_inference_single_image函数如下:

    def fast_rcnn_inference_single_image(
            boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
    ):
        """
        Single-image inference. Return bounding-box detection results by thresholding
        on scores and applying non-maximum suppression (NMS).
    
        Args:
            Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes
            per image.
    
        Returns:
            Same as `fast_rcnn_inference`, but for only one image.
        """
        valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
        indices = torch.arange(start=0, end=scores.shape[0], dtype=int)
        indices = indices.expand((scores.shape[1], scores.shape[0])).T
        if not valid_mask.all():
            boxes = boxes[valid_mask]
            scores = scores[valid_mask]
            indices = indices[valid_mask]
        scores = scores[:, :-1]
        indices = indices[:, :-1]
    
        num_bbox_reg_classes = boxes.shape[1] // 4
        # Convert to Boxes to use the `clip` function ...
        boxes = Boxes(boxes.reshape(-1, 4))
        boxes.clip(image_shape)
        boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4)  # R x C x 4
    
        # Filter results based on detection scores
        filter_mask = scores > score_thresh  # R x K
        # R' x 2. First column contains indices of the R predictions;
        # Second column contains indices of classes.
        filter_inds = filter_mask.nonzero()
        if num_bbox_reg_classes == 1:
            boxes = boxes[filter_inds[:, 0], 0]
        else:
            boxes = boxes[filter_mask]
    
        scores = scores[filter_mask]
        indices = indices[filter_mask]
        # Apply per-class NMS
        keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
        if topk_per_image >= 0:
            keep = keep[:topk_per_image]
        boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
        indices = indices[keep]
    
        result = Instances(image_shape)
        result.pred_boxes = Boxes(boxes)
        result.scores = scores
        result.pred_classes = filter_inds[:, 1]
        result.indices = indices
        return result, filter_inds[:, 0]

    c) 安装;如遇到问题,请参考detectron2;不同操作系统安装有差异

    cd detectron2
    pip install -e .

    测试

    a) 预训练模型下载

    wget https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/model_final_b1acc2.pkl

    b) 测试Grad-CAM图像生成

    ​ 在本工程目录下执行如下命令

    export KMP_DUPLICATE_LIB_OK=TRUE
    python detection/demo.py --config-file detection/faster_rcnn_R_50_C4.yaml \
    --input ./examples/pic1.jpg \
    --opts MODEL.WEIGHTS /Users/yizuotian/pretrained_model/model_final_b1acc2.pkl MODEL.DEVICE cpu

    Grad-CAM结果

    原始图像 检测边框 Grad-CAM HeatMap Grad-CAM++ HeatMap 边框预测类别
    Dog
    Aeroplane
    Person
    Horse

    总结

    ​ 对于目标检测Grad-CAM++的效果并没有比Grad-CAM效果好,推测目标检测中预测边框已经是单个对象了,Grad-CAM++在多个对象的情况下优于Grad-CAM

    目标检测-retinanet

    ​ 在目标检测网络faster r-cnn的Grad-CAM完成后,有两位网友abhigoku10wangzyon问道怎样在retinanet中实现Grad-CAM。retinanet与faster r-cnn网络结构不同,CAM的生成也有一些差异;以下是详细的过程:

    detectron2安装

    a) 下载

    git clone https://github.com/facebookresearch/detectron2.git

    b) 修改detectron2/modeling/meta_arch/retinanet.py 文件中的inference_single_image函数,主要是增加feature level 索引,记录分值高的预测边框是由第几层feature map生成的;修改后的inference_single_image函数如下:

        def inference_single_image(self, box_cls, box_delta, anchors, image_size):
            """
            Single-image inference. Return bounding-box detection results by thresholding
            on scores and applying non-maximum suppression (NMS).
    
            Arguments:
                box_cls (list[Tensor]): list of #feature levels. Each entry contains
                    tensor of size (H x W x A, K)
                box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
                anchors (list[Boxes]): list of #feature levels. Each entry contains
                    a Boxes object, which contains all the anchors for that
                    image in that feature level.
                image_size (tuple(H, W)): a tuple of the image height and width.
    
            Returns:
                Same as `inference`, but for only one image.
            """
            boxes_all = []
            scores_all = []
            class_idxs_all = []
            feature_level_all = []
    
            # Iterate over every feature level
            for i, (box_cls_i, box_reg_i, anchors_i) in enumerate(zip(box_cls, box_delta, anchors)):
                # (HxWxAxK,)
                box_cls_i = box_cls_i.flatten().sigmoid_()
    
                # Keep top k top scoring indices only.
                num_topk = min(self.topk_candidates, box_reg_i.size(0))
                # torch.sort is actually faster than .topk (at least on GPUs)
                predicted_prob, topk_idxs = box_cls_i.sort(descending=True)
                predicted_prob = predicted_prob[:num_topk]
                topk_idxs = topk_idxs[:num_topk]
    
                # filter out the proposals with low confidence score
                keep_idxs = predicted_prob > self.score_threshold
                predicted_prob = predicted_prob[keep_idxs]
                topk_idxs = topk_idxs[keep_idxs]
    
                anchor_idxs = topk_idxs // self.num_classes
                classes_idxs = topk_idxs % self.num_classes
    
                box_reg_i = box_reg_i[anchor_idxs]
                anchors_i = anchors_i[anchor_idxs]
                # predict boxes
                predicted_boxes = self.box2box_transform.apply_deltas(box_reg_i, anchors_i.tensor)
    
                boxes_all.append(predicted_boxes)
                scores_all.append(predicted_prob)
                class_idxs_all.append(classes_idxs)
                feature_level_all.append(torch.ones_like(classes_idxs) * i)
    
            boxes_all, scores_all, class_idxs_all, feature_level_all = [
                cat(x) for x in [boxes_all, scores_all, class_idxs_all, feature_level_all]
            ]
            keep = batched_nms(boxes_all, scores_all, class_idxs_all, self.nms_threshold)
            keep = keep[: self.max_detections_per_image]
    
            result = Instances(image_size)
            result.pred_boxes = Boxes(boxes_all[keep])
            result.scores = scores_all[keep]
            result.pred_classes = class_idxs_all[keep]
            result.feature_levels = feature_level_all[keep]
            return result

    c) 修改detectron2/modeling/meta_arch/retinanet.py 文件增加predict函数,具体如下:

        def predict(self, batched_inputs):
            """
            Args:
                batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
                    Each item in the list contains the inputs for one image.
                    For now, each item in the list is a dict that contains:
    
                    * image: Tensor, image in (C, H, W) format.
                    * instances: Instances
    
                    Other information that's included in the original dicts, such as:
    
                    * "height", "width" (int): the output resolution of the model, used in inference.
                      See :meth:`postprocess` for details.
            Returns:
                dict[str: Tensor]:
                    mapping from a named loss to a tensor storing the loss. Used during training only.
            """
            images = self.preprocess_image(batched_inputs)
    
            features = self.backbone(images.tensor)
            features = [features[f] for f in self.in_features]
            box_cls, box_delta = self.head(features)
            anchors = self.anchor_generator(features)
    
            results = self.inference(box_cls, box_delta, anchors, images.image_sizes)
            processed_results = []
            for results_per_image, input_per_image, image_size in zip(
                    results, batched_inputs, images.image_sizes
            ):
                height = input_per_image.get("height", image_size[0])
                width = input_per_image.get("width", image_size[1])
                r = detector_postprocess(results_per_image, height, width)
                processed_results.append({"instances": r})
            return processed_results

    d) 安装;如遇到问题,请参考detectron2;不同操作系统安装有差异

    cd detectron2
    pip install -e .

    测试

    a) 预训练模型下载

    wget https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl

    b) 测试Grad-CAM图像生成

    ​ 在本工程目录下执行如下命令:

    export KMP_DUPLICATE_LIB_OK=TRUE
    python detection/demo_retinanet.py --config-file detection/retinanet_R_50_FPN_3x.yaml \
          --input ./examples/pic1.jpg \
          --layer-name head.cls_subnet.0 \
          --opts MODEL.WEIGHTS /Users/yizuotian/pretrained_model/model_final_4cafe0.pkl MODEL.DEVICE cpu

    Grad-CAM结果

    图像1 图像2 图像3 图像4
    原图
    预测边框
    GradCAM-cls_subnet.0
    GradCAM-cls_subnet.1
    GradCAM-cls_subnet.2
    GradCAM-cls_subnet.3
    GradCAM-cls_subnet.4
    GradCAM-cls_subnet.5
    GradCAM-cls_subnet.6
    GradCAM-cls_subnet.7
    GradCAM++-cls_subnet.0
    GradCAM++-cls_subnet.1
    GradCAM++-cls_subnet.2
    GradCAM++-cls_subnet.3
    GradCAM++-cls_subnet.4
    GradCAM++-cls_subnet.5
    GradCAM++-cls_subnet.6
    GradCAM++-cls_subnet.7

    注:以上分别对head.cls_subnet.0~head.cls_subnet.7共8个层生成Grad-CAM图,这8层分别对应retinanet分类子网络的4层卷积feature map及ReLu激活后的feature map

    总结

    a) retinanet的Grad-CAM图效果都不算好,相对来说中间层head.cls_subnet.2~head.cls_subnet.4相对好一点

    b) 个人认为retinanet效果不要的原因是,retinanet最后的分类是卷积层,卷积核实3*3,也就是说反向传播到最后一个卷积层的feature map上,只有3*3个单元有梯度。而分类网络或者faster r-cnn分类都是全连接层,感受全局信息,最后一个卷积层的feature map上所有单元都有梯度。

    c) 反向传播到浅层的feature map上,有梯度的单元会逐渐增加,但是就像Grad-CAM论文中说的,越浅层的feature map语义信息越弱,所以可以看到head.cls_subnet.0的CAM图效果很差。

    目标检测-fcos

    ​ 在目标检测网络faster r-cnn和retinanet的Grad-CAM完成后,有位网友linsy-ai 问道怎样在fcos中实现Grad-CAM。fcos与retinanet基本类似,因为它们整体网络结构类似;这里使用AdelaiDet 工程中的fcos网络,以下是详细的过程:

    AdelaiDet安装

    a) 下载

    git clone https://github.com/aim-uofa/AdelaiDet.git

    b) 安装

    cd AdelaiDet
    python setup.py build develop

    注意:1. AdelaiDet安装依赖detectron2,需要首先安装$\color{red}{detectron2}$

    ​ 2. fcos的不支持CPU,只支持GPU,请确保在$\color{red}{GPU环境}$下安装和测试

    测试

    a) 预训练模型下载

    wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth

    b) 测试Grad-CAM图像生成

    ​ 在本工程目录下执行如下命令:

    export CUDA_DEVICE_ORDER="PCI_BUS_ID"
    export CUDA_VISIBLE_DEVICES="0"
    python AdelaiDet/demo_fcos.py --config-file AdelaiDet/R_50_1x.yaml \
      --input ./examples/pic1.jpg \
      --layer-name proposal_generator.fcos_head.cls_tower.8 \
      --opts MODEL.WEIGHTS /path/to/fcos_R_50_1x.pth MODEL.DEVICE cuda

    Grad-CAM结果

    图像1 图像2 图像3 图像4
    原图
    预测边框
    GradCAM-cls_tower.0
    GradCAM-cls_tower.1
    GradCAM-cls_tower.2
    GradCAM-cls_tower.3
    GradCAM-cls_tower.4
    GradCAM-cls_tower.5
    GradCAM-cls_tower.6
    GradCAM-cls_tower.7
    GradCAM-cls_tower.8
    GradCAM-cls_tower.9
    GradCAM-cls_tower.10
    GradCAM-cls_tower.11
    GradCAM++-cls_tower.0
    GradCAM++-cls_tower.1
    GradCAM++-cls_tower.2
    GradCAM++-cls_tower.3
    GradCAM++-cls_tower.4
    GradCAM++-cls_tower.5
    GradCAM++-cls_tower.6
    GradCAM++-cls_tower.7
    GradCAM++-cls_tower.8
    GradCAM++-cls_tower.9
    GradCAM++-cls_tower.10
    GradCAM++-cls_tower.11

    注:以上分别对proposal_generator.fcos_head.cls_tower..0~head.cls_subnet.11共12个层生成Grad-CAM图,这12层分别对应fcos分类子网络的4层卷积feature map、组标准化后的feature map及ReLu激活后的feature map

    总结

    ​ 不总结了,看图效果吧!

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/yizt/Grad-CAM.pytorch

    发行版本

    当前项目没有发行版本

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